Application of a Graphical Image Pre-retrieval Method Based on Compatible Rough Sets to the Self-localization Method of Mobile Robots

With the gradual maturation of mobile robot technology, mobile robots are playing an important role in more and more fields, also from the former industrial supplies to thousands of households, and the premise of mobile robot's application in these fields is that mobile robots can achieve positioning autonomously. Therefore, it is of great importance to study autonomous localization techniques for mobile robots. In this paper, we investigate the application of a graphical image pre-retrieval method based on compatible rough sets to the self-localization of mobile robots and build a visual localization system for mobile robots. The key function of the system is autonomous localization, based on a known map, and global localization needs to be completed before specifying a target point. To address the shortcomings of traditional global localization which requires human intervention to complete, global localization without human intervention is achieved by combining a UWB module and two improved methods, in the navigation process, based on the localization information obtained by the EKF algorithm, the adaptive Monte Carlo algorithm fuses In the navigation process, based on the positioning information obtained by the EKF algorithm, the positioning accuracy is further improved by the adaptive Monte Carlo algorithm fusing the odometry, IMU, UWB and LIDAR to meet the requirements for high positioning accuracy in complex environments.